The uniquely challenging task of generating dances stylistically faithful to input music has been tackled by many researchers.
Many early approaches follow a motion retrieval paradigm [1]}, [2]}, [3]}, but tend to create unrealistic choreographies that lack the complexity of human dances.
Later works instead synthesize motion from scratch by training on large datasets [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]} and propose many modeling approaches, including adversarial learning, recurrent neural networks, and transformers.
Despite achieving impressive performance, many such systems are complex [5]}, [11]}, [6]}, [16]}, often involving many layers of conditioning and sub-networks.
In contrast, our proposed method contains a single model trained with a simple objective, yet offers both strong generative and editing capabilities without significant hand-crafted design.
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